We usually hear the expression "value added" associated with the amount of enhancement and aggregation of materials and labor that a company applies to its products during manufacturing. However, adding value to products and services is only one element of creating value to customers and shareholders. The other important element is “separation”. Both Lean and 80/20 use separation as a way to purify processes (just like in chemistry) and deliver more value. Lean principles are based on value creation from the customer’s point of view. As you identify the work steps, you eliminate barriers that prevent a smooth production flow. Continuous flow is characterized by rhythm or “takt time” and is dictated by customer demand (pull). Throughout the process, you isolate and remove steps that do not add value or create waste (“muda”). In other words, you must have a free-of-interferences, undisturbed, customer-focused process to achieve lean. You need to separate waste from value-adding steps. Similarly, the 80/20 Business Process places an ultra-high focus on select groups of customers and products, avoiding distractions to the core work. The extreme focus helps capitalize on natural imbalances, implied by the 80/20 Principle. The separation part keeps the complexity created by the “trivial many” from contaminating the “vital few”. It turns the “eighty” into a center of attention. Separation is what keeps the “chaff away from the wheat”. It’s an important management concept and is applied to create focus, as well to impart autonomy. It’s not meant to build walls and create isolated fiefdoms. The main reasons to apply separation are:
Managers should understand (and make up their minds about) what is important and what is not. At a minimum, they should select the core segments to compete, the key customers to partner, the central products to lead with and the fundamental business processes to deliver value. Once they select the vital few, managers can separate and nurture these core areas, by asking the following question: how to increase the separation degrees between the core and the rest? To separate a core market segment from all others, for example, you can start by creating a dedicated sales and marketing organization for that segment. You can also set up distinct production lines and generate a “core segment P&L” to monitor progress. These are all part of what I call the first separation stage or degree. It’s one framework to build centers of attention around core activities. Add another separation degree and create autonomous business units, focusing on the core segments. The third stage is to operate with completely separate and autonomous companies, such as Berkshire Hathaway and most private equity firms do. Separation stages are closely coupled with the way companies grow. Stage three companies grow like a forest, spreading out and diversifying. Stage one companies bloom and expand like a shrub. In the same vein, we can say that stage two companies grow like trees, or they branch out into new market segments. Forests grow in fractal ways, or in patterns that repeat themselves at different scales (self similarity). We can see the resemblance of a forest to stage three companies, diversifying into different segments and industries, while keeping the value-creation model intact throughout different companies (self similarity). Call it satellite companies, divisions, business units, focused plants or service centers, the distributed and decentralized organization is better prepared to deliver value over the long haul. If executed with methodologies such as lean and 80/20, separation and decentralization have the potential to transform the business into a network of value-creating nodes. The benefits of this operating model are reflected in this quote from the 1979 letter from Warren Buffett to investors, which talks about his faith and confidence in the independence and empowerment of business units. The thinking can be summarized in this statement: “If you love the management, set them free”. “To the Shareholders of Berkshire Hathaway Inc.: Your company is run on the principle of centralization of financial decisions at the top (the very top, it might be added), and rather extreme delegation of operating authority to a number of key managers at the individual company or BU level. We could just field a basketball team with our corporate headquarters group (which utilizes only about 1,500 square feet of space). This approach produces an occasional major mistake that might have been eliminated or minimized through closer operating controls. But it also eliminates large layers of costs and dramatically speeds decision-making. Because everyone has a great deal to do, a very great deal gets done. Most important of all, it enables us to attract and retain some extraordinarily talented individuals—people who simply can’t be hired in the normal course of events—who find working for Berkshire to be almost identical to running their own show. We have placed much trust in them—and their achievements have far exceeded that trust.” [i] Some of the best performing companies in the world are decentralized: Berkshire Hathaway, GE, ITW, J&J and many others. They’ve evolved from conventional and monolithic structures to networks of companies and business units, while keeping their original values intact. The leaders realized early on that they needed to move from the “top of the hierarchy to the center of the network”. They went from “command and control” to “influence and connectivity”. They fostered segmentation and enabled value-creating nodes in the network to grow and to multiply. But let’s not get lost in the debate about centralization versus decentralization. You don’t need to wait until you are completely decentralized to start applying separation, as a management principle. You can start right away by selecting core areas and deciding how many degrees of separation to apply. The separation mentality can at a minimum be applied to customers, products and process, as shown in the table below. The key when applying separation to customers is to identify and place an extreme focus on the few customers that account for eighty percent of sales and margins, as well as on the few “twenty” ones that have strategic value to the business. Know everything there is to know about your “eighty” customers. Walk in their shoes frequently to understand their pain points and needs, and involve them in your product innovation efforts. The degrees of separation amongst customers start with sales and support differentiation, passing by the application of unique commercial policies, all the way to rechanneling and firing “twenty” customers, if necessary.
Many companies already treat their “eighty” customers special. At least they should. But the vast majority doesn’t clearly define the borders between core and the others, lacking defined ways to create separation, such as dedicated account teams and unique commercial policies. All customers need to be treated fairly, but treating all customers equally can lead to disaster. Stage three companies have separate value chains that align distinctly with “eighty” and “twenty” customers. The value created by each separate chain is proportional to what they deliver and to the complexity (overhead) each business has to live with. In most cases, customers are clearly told the difference between these different chains and appreciate getting a fair value for what they are buying. When it comes to products and processes, there are many ways to create separation. Starting from an extreme focus on “eighty” products, all the way to investing in separate businesses to distinctly deal with core and non-core. It means separating the mainstream, with less variation, from the infrequent products, which have greater complexity. Detached businesses have different value chains and are uniquely positioned in the market. Another separation technique is to outsource non-core products to specialized suppliers. Outsourcing makes it simpler to place a value on these items. The costs to make them available to customers come listed in the supplier invoice, while the internal costs of low-volume or specialty products are not always accurate, due to hidden complexity. The first separation degree at the shop floor is to distinguish the production methods between core and non-core. The “eighty” needs to be made in the most efficient way possible. This can be attained with lean manufacturing techniques, such as one-piece-flow, takt time and pull systems. A separation method used in lean, is to uncouple customer demand (pull) from orders to suppliers, improving production flow and rhythm. At a higher degree, companies use 80/20 techniques, such as inlining of high-volume products and MRD (market rate of demand) to further optimize and simplify production of the “eighty”. 80/20 also separates period costs from variable costs, when building products, improving the accuracy of contribution margins. Third separation degree is achieved by investing in discrete business units, or separate companies, to manufacture the core away from the low-volume or specialty. By having isolated plants and P&Ls for low-volume and specialty, it’s possible to understand the real cost of complexity, thus pricing correctly in the market. As the business expands, it will segment further and separate more complexity away from the core. But when we carve out a new business, plant or production line, it’s important to consider its ability to add value as a stand-alone unit. In order to do this, I have four types of questions that I ask the team and myself, which I consider the pillars for creating a new node in the network:
To finalize, I believe that most companies know the difference between core and trivial businesses. The problem is that managers don’t take steps to separate them, allowing the trivial to contaminate the core. The word “focus” is constantly used (or misused), but in many cases, there is no action behind it. What does it mean to increase focus? What is needed? Separation works because it forces the opportunity to stand out. It places a new visible node in the value-creation network. So let’s put some meaning behind the word “focus” and enhance value-creation not only by adding parts and labor to our products, but also by simplifying our business and putting some distance between what makes it great and what makes it go. [i] Warren E. Buffett, 1979 letter to Berkshire Hathaway investors (published March 3, 1980), http://www.berkshirehathaway.com/letters/1979.html - Retrieved on October, 2015.
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There is a better way to create plans using business analytics (BA) and 80/20. Organizations of all sizes should take steps to explore the wealth of information available and commit to data-driven planning and decision-making. They should also rethink the yearly budgeting process, to make it more adaptive and analytical. The goal is to have a leaner and more robust process, delivering greater accuracy and agility. Companies create budgets or annual operating plans (AOP), with a large focus on financial targets. For the most part, they aim to reach strategic milestones and close gaps. But typically, they end up with plans that emphasize negotiated objectives and boundaries, with a lot less focus on optimization and transformation. Budgets by and large, become a financial exercise in compromising. Jack Welch (former Chairman of GE) calls the budgeting process “the most ineffective practice in management”. He goes on to say that the budget process is an “exercise in minimization. You’re always getting the lowest out of people because everyone is negotiating to get to the lowest number”. Accuracy, relevance and execution ability are the greatest weaknesses of typical annual plans. During preparation, budgets consume too much of management’s attention and can become irrelevant very quickly as conditions change (and they always do). Budgets are also known to be fixed and inflexible. To make it worse, most companies tie executive compensation to performance against the budget and not against previous years or industry benchmarks. Not to mention that a huge amount of effort and time is placed on budget preparation and data gathering. In fact, more data gathering than analyses. Plan iterations are usually the outcome of negotiating sessions amongst managers, as opposed to teamwork and analysis of contextual information than leads to foresight. Managers spend too much time bargaining and setting boundaries, and not enough time planning for execution. No wonder many companies fail due to lack of effective operating and strategic plans. The solution is to create an adaptive and analytical performance management process. Instead of cramming the entire exercise into annual planning seasons and quarterly or semi-annual reviews, companies should uncouple forecasting and financial analyses from planning itself, and make them ongoing activities of the organization. Once you uncouple them, what is left for the planning season is work that is more prescriptive (how to make it happen), as opposed to data gathering and number crunching. The year-round activities (reporting, analyses and forecasting) receive a large dose of intelligence using BA, while the once-a-year activity (planning) is redefined to focus on more prescriptive planning. The benefits are more adaptability, accuracy and relevance to the organization. Once you uncouple the planning elements, you end up with three parts:
Step two above (rolling forecasts based on analytics) is key to create an ongoing cycle of planning, execution and evaluation. It will also add intelligence to the process by tying the forecast to key business drivers. These two elements will make the company more agile and dynamic in the planning process. Rolling forecasts coupled with analytics, reduce uncertainty in planning and forecasting while encouraging constant assessment of the plan throughout the organization. A good example of a company that benefits from this approach is Southwest Airlines. Given the volatility in the airline industry, they’ve opted for a 12-month rolling forecast and quarterly planning. This approach has worked very well for the company, which has been consistently profitable for over 30 years, in a very tough industry. It’s also important to blend actual performance with analytics, to succeed. Forecasts need to be created rapidly and with less complexity and cost. Therefore it’s important to structure the data and the analyses, providing a decent level of automation. Bear in mind that analytics are not the sole responsibility of the IT department, just as budgeting is not an exclusive responsibility of the finance department. BA is a capability that needs to be developed by all functional areas and business units, while IT provides access to data and analytical tools. Typical areas to apply predictive analytics are as follows: Predictive analytics is by no means exclusive to e-businesses and is already penetrating brick and mortar manufacturing and distribution companies. Data is easy to collect and to access. The millennial generation, or the tech savvy generation, is taking over. New and more cost effective tools are available and companies are evolving quickly to more advanced levels. A few leading edge firms are already using prescriptive analytics. So allow me to take a moment to explain the different types of analytics. The first type is known as descriptive, and is used by the majority of companies, in more or less structured ways. It is done with standard and ad hoc reports and financial analysis. They look at history and tell you what happened to the business, how often it happens and derive actions to fix problems. Companies have different ways to extract insight and to diagnose the reasons why something happened. Management uses KPIs and other indicators that point to root causes and provide clues about potential actions to stop a problem from happening, and to improve the results. But keep in mind, this is all looking at the rear-view mirror and explaining what happened. While this approach can provide reasonable insight, the knowledge gained is all in hindsight and the future depends solely on critical analysis and management experience. The second category of BA is known as predictive analytics. It uses data mining techniques to look for hidden patterns and correlations that may exist in contextual data, in order to derive predictors that will point to the future. Today, e-commerce companies are the primary users of these tools. However, more companies are waking up to the power of predictive analytics. On top of the performance improvement potential by this method, there is an incredible opportunity for manufacturers to improve the longevity and reliability of products and production lines, for example, using predictors for maintenance and early product failure detection. The most advanced type is prescriptive analytics. It’s used to find the best courses of action for a given situation, feeding from both descriptive and predictive types. It’s a natural evolution for companies that are consistently mining and analyzing data, and trying to establish correlations and trends. It presents you with actionable options to develop data based optimization plans. Prescriptive analytics is an evolving field and advanced techniques are being tested, including the use of artificial intelligence software, to use both structured and unstructured data elements. You can start applying the prescriptive mindset right away, through mining the data that is already in your servers. In fact, 80/20 analytics is based on simple ways to evaluate correlated sets of data, such as products and customers, to arrive at optimization strategies. The picture below shows the three areas required to develop an adaptive planning process using business analytics. Many predictors are similar to KPIs used by managers. The difference is that most KPIs are set based on historical analysis and goals, while predictors are set based on data analytics, for relevance and insight. They allow you to set smart goals, while keeping the organization focused on fewer issues. Although Google Analytics is a tool for online businesses, it’s a good example of how to use prescriptive analytics. Google offers users a Prediction API (application program interface) that automatically provides pattern matching and machine learning capabilities. After it learns from a set of training data, the API predicts future trends, detect changes in usual patterns and recommend actions. It does amazing things with remarkable accuracy, like predicting spending patterns by online shoppers, customer attitude towards a certain brand, based on positive or negative feedback, and so much more. I believe such tools will become mainstream, at all types of industries, in the very near future.
While both predictive and prescriptive analytics are often associated with big-data and sophisticated algorithms, the basic principles behind these tools are really down-to-earth and useful to managers trying to focus on what is important (the “eighty”), mitigating risks and knowing which levers are most effective, in case they have a problem during execution. Planning with 80/20 analytics requires that we spend time upfront analyzing contextual data and identifying the few key pressure points and optimization actions. Let me give you a simplified practical application how a distribution business can improve its profitability outlook by applying adaptive planning and analytics. Like manufacturing companies, distribution companies want to improve the return on invested capital (ROIC) or, at a minimum, the return on assets (ROA). ROIC is a profitability ratio that tells us how good a company is at turning capital into profits. Companies want ROIC percentage to be attractive, so investors can continue to allocate capital. ROA is an indicator of how profitable a company is relative to its total assets, and a subset of ROIC, but mainly useful for distribution businesses, since most of the investment sits in the inventory part of the assets. In order to improve these metrics, there are two strategies available for the business: 1) to increase return on sales in terms of EBIT (earnings before interest and taxes) or 2) to turn its assets faster (or reduce the assets applied). Based on our experience, a reduction in assets (in terms of inventory and accounts receivables) or invested capital (CAPEX), has to be way beyond normal (or even healthy) levels in order to significantly impact ROA and ROIC. They can impact free cash flow positively in the short run, but will not necessarily make the business better. The other way to do this is to improve the fitness level of the company, by increasing the efficiency reflected in the Productivity Index (PI), which was discussed on a previous article. PI is the ratio between contribution margin dollars and overhead dollars. The crux of the issue is to know which levers are most effective to increase ROIC (pricing, overhead cuts, inventory reduction, etc.), while continuing to march towards profitable growth and value creation goals. In other words: what is the art of the possible without damaging the business? Through the application of business analytics that is connected with a rolling forecast, companies can arrive at an optimization plan that makes these apparently conflictive goals work together. In most cases, distributors find the following contextual data sets to be the most useful: markets and sales, customers and products, product lines and suppliers, contribution margins and overhead, inventory and contribution margins. Without a dose of foresight on how certain actions impact the P&L, managers can destroy value. It becomes a costly trial and error process. One of the common mistakes, for example, is to go after additional contribution margin dollars by lowering prices across the board, in order to increase sales (making it up with volume). The problem is that you need sales to increase significantly more to offset the margin reduction from price cuts. It is not a realistic scenario in most cases – it’s a “fools game”. Another common mistake is to underprice the tail end of the product line. In other words, distributors tend to be under compensated for investing in inventory or not to get paid enough for managing complexity on customer’s behalf. Sales analytics can help you avoid these errors, by recommending and predicting the actions’ impact on margins and ROIC. It tells you how much portfolio momentum you have, so you can take appropriate pricing actions. It also performs inventory investment analysis (turns and earns) and recommends a pricing strategy that is different for different groups of products, depending on how much contribution margin they generate and how fast they turn. Based on predictive models, managers can develop viable optimization plans, to improve the business and avoid deadly mistakes. Examples of such actions could include: a) increase prices and contribution margins selectively, using 80/20 analytics to define the different pricing levels for different products; b) scaling down overhead in selective areas of the business, allowing SG&A expenses to increase at a lower rate than sales (for example, below 2% sales increase); c) increase sales in certain market segments, which are projected to have above industry growth rates; d) recommend the optimal parts mix in inventory to maximize turns and earns, based on demand patterns and trends. At the end, the plan is still about setting priorities, improving contribution margins, reducing overhead, optimizing investment in inventory and looking for the best market segments to grow. What’s new here is how the actions can be combined, with the right amount of intensity, to produce maximum results. Could we thrive without a better planning process and business analytics? We could probably survive, but given the need for agility and the pace of change, the ability to react faster and apply analytics are key differentiators in most industries. |
AuthorPedro Ferro Archives
August 2016
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